Short answer: R is no longer “just a statistics language.” In 2026, it has become a serious, practical, production‑ready tool for AI and machine learning, especially for analysts, researchers, and solo developers who want fast results without heavy engineering overhead. Below is the full breakdown. 🚀 1. R is built for data — the foundation of all AI AI systems live or die based on data quality. R gives you: · tidyverse for clean, readable data pipelines · dplyr for fast transformations · data.table for high‑performance operations · ggplot2 for world‑class visualizations This makes R one of the best environments for: · feature engineering · exploratory data analysis · dataset cleaning · statistical validation Before you train a model, you need clean data — and R is unmatched here. 🤖 2.…